A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics‐Based COVID‐19 Diagnosis

Since the outbreak of Coronavirus Disease 2019 (COVID‐19), the virus has posed a grave threat to human health. Automated segmentation of COVID‐19 lung computed tomography (CT) scans is a crucial diagnostic tool that aids physicians in providing accurate and timely diagnoses, as it contains significant radiomics information. Given that the specificity for discriminating between the causes of conventional pulmonary features is lower than its sensitivity, the primary goal of this study is to develop and evaluate a CT‐based radiomics model capable of distinguishing between COVID‐19 and other lung diseases. To address this, we propose an efficient, modified radiomics feature processing method that integrates an optimal aerial perspective (OAP) parameter‐based intensity dark channel prior (IDCP) with a 50‐layer residual deep neural network (ResNet50 DNN) for autolesion segmentation (ALS‐IOAP‐DNN). To further enhance COVID‐19 lesion estimation, novel optimization strategies, including a hybrid simulated annealing‐cuckoo search (SA‐CS) algorithm, are introduced alongside the original SA method. The SA‐CS algorithm extends SA by preventing entrapment in local minimum and enhancing global exploration. Five benchmark functions are used to accelerate convergence and address the issue of local optima. As a result, the hybrid approach outperforms 10 recent studies on two publicly available datasets (COVID‐CT‐Dataset and HUST‐19), achieving an average accuracy score of 100% across different epochs, along with perfect accuracy, 100% sensitivity, and 100% specificity. The proposed models significantly outperform the baseline model, with accuracy improvements of 13.6% on Data1 and 2.5% on Data2. While the baseline model achieves 88% accuracy on Data1 and 97.6% on Data2, the proposed ALS‐IOAP‐DNN4 model attains perfect accuracy (100%) on both datasets, demonstrating the effectiveness of ALS and advanced optimization techniques. Furthermore, the use of OAP with IDCP enhances the precision of COVID‐19 lesion estimation, underscoring its significance in COVID‐19 diagnosis and medical imaging management.